Final answer:
To minimize data transfers in Apache Spark, using in-memory storage, increasing data partition size, and optimizing data compression algorithms are effective strategies. Decreasing the number of worker nodes may lead to resource bottlenecks and is not advisable.
Step-by-step explanation:
When working with Apache Spark, minimizing data transfers is crucial for improving performance and reducing network load. Here are several strategies that can help achieve this:
- Use in-memory storage: Spark's in-memory processing capabilities allow data to be stored in RAM across your cluster's nodes, making it faster to access and eliminating the need for repeated disk I/O operations.
- Increase the data partition size: By having larger partitions, the number of transfers can be decreased as data is more localized. However, care must be taken not to make partitions so large that they do not fit in memory or cause skewed workloads.
- Optimize the data compression algorithm: Using efficient compression algorithms can reduce the volume of data being transferred without losing information, thus using the network bandwidth more effectively.
- Decreasing the number of worker nodes is generally not a good strategy, as it can lead to resource bottlenecks and diminish the distributedprocessing capabilities of the cluster.
Therefore, options a, b, and c can help minimize data transfers, each in different ways. The right approach depends on the specific context and requirements of the Spark application.